Tell me about a time you identified a data-related bottleneck or quality issue in a business process. How did you uncover it, what analysis did you perform, and what measurable improvement resulted from your solution?

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VirtualInterview.AI · Accepted Answer

In my last role, our weekly sales forecast was always late and constantly revised. On paper, the process was fine, but cycle time was 3+ days and accuracy hovered around 70%. I started by mapping the end-to-end data flow and logging timestamps at each handoff. The logs showed 60% of the delay came from manual Excel reconciliations due to mismatched customer IDs between CRM and billing.

I pulled a month of data, profiled key fields, and found about 12% of records had inconsistent IDs or naming conventions. I built a Python-based matching script using fuzzy joins and a reference mapping table, then moved the reconciliation into a scheduled SQL job. We also added simple validation checks (missing IDs, duplicate invoices) and a daily data-quality report to Slack.

Forecast prep time dropped from 3 days to under 8 hours, and forecast accuracy improved from ~70% to 88% over the next quarter. Finance highlighted it as a key reason they could lock the forecast a day earlier.

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